Inference produces millions of bounding boxes about deeplung HOT 7 CLOSED

#!/bin/bash
set -e

# python prepare.py
cd detector
maxeps=150
f=9
CUDA_VISIBLE_DEVICES=0,1

/usr/bin/python3.5 main.py --model res18 -b 4  --save-dir res18/retrft96$f/ --epochs $maxeps --config config_training$f

def hard_mining(neg_output, neg_labels, num_hard):
    print('neg_output: {}'.format(len(neg_output)))
    print('num_hard: {}'.format((num_hard)))

    _, idcs = torch.topk(neg_output, min(num_hard, len(neg_output)))
    neg_output = torch.index_select(neg_output, 0, idcs)
    neg_labels = torch.index_select(neg_labels, 0, idcs)
    return neg_output, neg_labels

Train phase
Batch size: 4
neg_output: 0
num_hard: 8
Traceback (most recent call last):
  File "main.py", line 402, in <module>
    main()
  File "main.py", line 212, in main
    train(train_loader, net, loss, epoch, optimizer, get_lr, args.save_freq, save_dir)
  File "main.py", line 234, in train
    loss_output = loss(output, target)
  File "/usr/local/lib/python3.5/dist-packages/torch/nn/modules/module.py", line 357, in __call__
    result = self.forward(*input, **kwargs)
  File "/home/shlomo/db/dev/baidu-2018/detector/layers.py", line 182, in forward
    neg_output, neg_labels = hard_mining(neg_output, neg_labels, self.num_hard * batch_size)
  File "/home/shlomo/db/dev/baidu-2018/detector/layers.py", line 153, in hard_mining
    _, idcs = torch.topk(neg_output, min(num_hard, len(neg_output)))
RuntimeError: dimension specified as -1 but tensor has no dimensions